Classificação de Sotaques Brasileiros usando Redes Neurais Profundas
Keywords: Accent Recognition
AbstractThe automatic classification of accents has several potential applications, for instance, the identification and authentication of users, forensic investigation tools and the selection of specialized models in text-to-speech and speech-to-text systems. In this work, several architectures of artificial neural networks were evaluated in the problem of accent classification. The performance of these architectures was compared with the methods GMM-UBM, GMM-SVM and iVector. Experimental results show that 5 out of 6 architectures achieve better values of accuracy, precision and recall than the previous methods. The best architecture reached 90\% of accuracy, with precision, recall and F1-score of 0.92, 0.84 and 0.87, respectively.